Can I use an autoencoder with high latent representational space?
i feel , i can answer this question, based the web source that i found and i read. that can use an autoencoder with hight latent representational space. example use LSTM autoencoder, LTSM autoencoder used for sequence or time series data. on the source, they use CNN autoencoder to denoise some syntetic noised data they have generated. but they have asked what is the meaning of this latent representation space? for what they have done before. on that answer. they said"your input data is noisy sinewave data. your are not supposed to use Convolutional autoencoder for sequence data".

https://stackoverflow.com/questions/59438488/cnn-autoencoder-latent-space-representation-meaning

addition other source for answer this question
https://towardsdatascience.com/understanding-latent-space-in-machine-learning-de5a7c687d8d
https://towardsdatascience.com/deep-inside-autoencoders-7e41f319999f

hopefully help answer :))